Improving regrasp algorithms to analyze the utility of work surfaces in a workcell

Abstract

The goal of this paper is to develop a regrasp planning algorithm general enough to perform statistical analysis with thousands of experiments and arbitrary mesh models. We focus on pick-and-place regrasp which reorients an object from one placement to another by using a sequence of pick-ups and place-downs. We improve the pick-and-place regrasp approach developed in 1990s and analyze its performance in robotic assembly with different work surfaces in the workcell. Our algorithm will automatically compute the stable placements of an object, find several force-closure grasps, generate a graph of regrasp actions, and search for regrasp sequences. We demonstrate the advantages of our algorithm with various mesh models and use the algorithm to evaluate the completeness, the cost and the length of regrasp sequences with different mesh models and different assembly tasks in the presence of different work surfaces. Our results show that spare work surfaces are beneficial to assembly. Tilted work surfaces are only sometimes beneficial, depending on the objects.